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Thermo Fisher
Tsq Quantis

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Lab Devices
Thermo Fisher
Tsq Quantis

Analyze TSQ Quantis Triple Quadrupole MS data with AI

Targeted quantification of specific compounds with high sensitivity

Mass Analyzer: Triple quadrupole for SRM/MRM

Sensitivity: Attogram-level detection

Ionization: Polarity switching capability

Performance: Robust quantitative analysis

Data Export: RAW files, CSV quantitation

Get Started For Free Fully compatible with Sourcetable

Quick Start Integration

Get your TSQ Quantis data into Sourcetable in 3 simple steps

Export Data

Export MRM quantitation results from your TSQ Quantis via TraceFinder/Xcalibur to CSV
Upload to Sourcetable

Import your targeted quantification data into Sourcetable for analysis
Start Analysis

Perform automated MRM peak integration, calibration curve fitting, and batch quantification

File Format Compatibility

Optimized for targeted quantification with MRM analysis and pharmaceutical quality control

.csv

Raw Data

Full Support MRM peak integration, calibration curves, internal standard normalization, LOD/LOQ reporting

.raw

Processing Required Requires processing via TraceFinder or Xcalibur

Pre-built Analysis Workflows

Targeted workflows for pharmaceutical analysis and bioanalytical method development

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Targeted Quantification

High-sensitivity MRM analysis for specific compound quantification

MRM Targeted Analysis Quantification High Sensitivity

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Pharmaceutical QC

Drug quantification and impurity analysis for quality control

Pharmaceutical Quality Control Drug Analysis Impurities

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Bioanalytical Methods

Method validation and calibration curve analysis

Method Validation Calibration Bioanalysis Pharmacokinetics

Technical Requirements

Software Dependencies

TSQ Quantis Triple quadrupole LC-MS system
Control Software TraceFinder or Xcalibur
File Export Format CSV (quantitation), RAW (native)
Applications Pharmaceutical QA, clinical chemistry

Sourcetable Libraries

Data Processing pandas, numpy
Statistical Analysis scipy, statsmodels
Visualization matplotlib, plotly
Analytical Chemistry scikit-learn, scipy.optimize

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